
import numpy as np
from sklearn.decomposition import PCA

class ResourceAdapter:
    """资源自适应扩展模块"""
    def __init__(self, entropy_threshold=0.7):
        self.entropy_threshold = entropy_threshold
        
    def lightweight_deployment(self, global_model, client_data, temperature):
        """轻量化模型部署 (3.4节)"""
        # 通道剪枝 (简化实现)
        pruned_model = global_model * np.random.binomial(1, 0.7, size=global_model.shape)
        
        # 知识蒸馏
        teacher_logits = global_model @ client_data.T
        student_logits = pruned_model @ client_data.T
        kl_loss = np.sum(teacher_logits * np.log(teacher_logits/student_logits + 1e-10))
        
        # 温度调节约束强度
        return pruned_model * (1 - temperature * kl_loss)
    
    def comm_compression(self, gradients, entropy_values, ratio=0.2):
        """通信流量弹性压缩 (3.4节)"""
        # 选择熵值最高的Top-K参数
        top_indices = np.argsort(entropy_values)[-int(len(entropy_values)*ratio):]
        compressed_grad = gradients[top_indices]
        
        # 残差差分编码 (简化实现)
        diff = compressed_grad - np.mean(compressed_grad)
        return diff, top_indices
    
    def compute_scheduling(self, temperature, base_epochs=5):
        """温度协调的计算负载调度 (3.4节)"""
        if temperature > 0.5:  # 高温阶段
            return base_epochs, "full"
        else:  # 低温阶段
            return 1, "selective"  # 仅首轮完整计算
